20251221 - shorten output

Author: isaactpetersen

hlm.qmd

@@ -198,7 +198,10 @@ ranef(linearMixedModel)
 ```{r}
 #| echo: false
 
-head(ranef(linearMixedModel))
+linearMixedModel_re <- ranef(linearMixedModel)
+
+linearMixedModel_re$id <- head(linearMixedModel_re$id)
+linearMixedModel_re
 ```
 
 #### `nlme`
@@ -365,7 +368,10 @@ ranef(quadraticGCM)
 ```{r}
 #| echo: false
 
-head(ranef(quadraticGCM))
+quadraticGCM_re <- ranef(quadraticGCM)
+
+quadraticGCM_re$id <- head(quadraticGCM_re$id)
+quadraticGCM_re
 ```
 
 ### Spline Growth Curve Model {#splineGCM}
@@ -479,7 +485,10 @@ ranef(splineGCM)
 ```{r}
 #| echo: false
 
-head(ranef(splineGCM))
+splineGCM_re <- ranef(splineGCM)
+
+splineGCM_re$id <- head(splineGCM_re$id)
+splineGCM_re
 ```
 
 # Generalized Linear Mixed Models {#generalized}

multipleImputation.qmd

@@ -521,13 +521,18 @@ mice::predict_mi(
 ```{r}
 #| echo: false
 
-mice::predict_mi(
+fit_lm_fitted <- mice::predict_mi(
   fit_lm,
   #newdata = # can include if want to make predictions based on another (i.e., "new") data object
   se.fit = TRUE,
   interval = c("prediction")
-) %>% 
-  head()
+)
+
+fit_lm_fitted$fit <- head(fit_lm_fitted$fit)
+fit_lm_fitted$se.fit <- head(fit_lm_fitted$se.fit)
+fit_lm_fitted$df <- head(fit_lm_fitted$df)
+
+fit_lm_fitted
 ```
 
 ```{r}
@@ -544,13 +549,18 @@ mice::predict_mi(
 ```{r}
 #| echo: false
 
-mice::predict_mi(
+fit_lmer_fitted <- mice::predict_mi(
   fit_lmer,
   #newdata = # can include if want to make predictions based on another (i.e., "new") data object
   se.fit = TRUE,
   interval = c("prediction")
-) %>% 
-  head()
+)
+
+fit_lmer_fitted$fit <- head(fit_lmer_fitted$fit)
+fit_lmer_fitted$se.fit <- head(fit_lmer_fitted$se.fit)
+fit_lmer_fitted$df <- head(fit_lmer_fitted$df)
+
+fit_lmer_fitted
 ```
 
 # Resources

regression.qmd

@@ -1384,6 +1384,7 @@ Resources
 Spearman's rho is a non-parametric correlation.
 
 ```{r}
+
 cor.test(
   mydata$bpi_antisocialT1Sum,
   mydata$bpi_antisocialT2Sum) #Pearson r, correlation that is sensitive to outliers
@@ -1396,19 +1397,49 @@ cor.test(
 ####### Minimum vollume ellipsoid
 
 ```{r}
+#| eval: false
+
 cov.mve(
   na.omit(mydata[,c("bpi_antisocialT1Sum","bpi_antisocialT2Sum")]),
   cor = TRUE)
 ```
 
+```{r}
+#| echo: false
+
+cov.mve.fit <- cov.mve(
+  na.omit(mydata[,c("bpi_antisocialT1Sum","bpi_antisocialT2Sum")]),
+  cor = TRUE)
+
+cov.mve.fit$best <- head(cov.mve.fit$best)
+cov.mve.fit
+```
+
 ####### Minimum covariance determinant
 
 ```{r}
+
+```
+
+```{r}
+#| eval: false
+
 cov.mcd(
   na.omit(mydata[,c("bpi_antisocialT1Sum","bpi_antisocialT2Sum")]),
   cor = TRUE)
 ```
 
+```{r}
+#| echo: false
+
+cov.mcd.fit <- cov.mcd(
+  na.omit(mydata[,c("bpi_antisocialT1Sum","bpi_antisocialT2Sum")]),
+  cor = TRUE)
+
+cov.mcd.fit$best <- head(cov.mcd.fit$best)
+cov.mcd.fit
+```
+
 ####### Winsorized correlation
 
 ```{r}